CodeBERT-base

Training Data

Training Objective

Usage

Reference

1. CodeBERT trained with Masked LM objective (suitable for code completion)
2. 🤗 Hugging Face's CodeBERTa (small size, 6 layers)

Citation

@misc{feng2020codebert,
    title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
    author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou},
    year={2020},
    eprint={2002.08155},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

• Original Model Card
• feature-extraction Task on Hugging Face

Send Request

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Content-Type: application/json" \
    -d '{"inputs":"Today is a sunny day and I will get some ice cream."}'

Supported Parameters

• inputs (string or string[]): Either the text to create the embeddings for, or a list of texts.
• normalize (boolean): Whether to normalize the embedding to be generated or not.
• prompt_name (string): The name of the prompt that should be used by for encoding. If not set, no prompt will be applied. Must be a key in the sentence-transformers configuration prompts dictionary. For example if prompt_name is "query" and the prompts is {"query": "query: ", …}, then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text to encode.
• truncate (boolean): Whether to truncate the input to match the max allowed input tokens or not.
• truncation_direction (enum): Possible values: Left, Right.